Overview

Dataset statistics

Number of variables20
Number of observations6939
Missing cells72646
Missing cells (%)52.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 MiB
Average record size in memory160.0 B

Variable types

Numeric18
Categorical2

Alerts

Country Name has a high cardinality: 217 distinct values High cardinality
CPI human resources is highly correlated with CPIA gender equality and 1 other fieldsHigh correlation
CPIA gender equality is highly correlated with CPI human resources and 5 other fieldsHigh correlation
CPIA social protection is highly correlated with CPI human resources and 2 other fieldsHigh correlation
fertility rate is highly correlated with CPIA gender equality and 5 other fieldsHigh correlation
intentional homicides is highly correlated with poverty gap and 1 other fieldsHigh correlation
literacy rate is highly correlated with CPIA gender equality and 4 other fieldsHigh correlation
poverty gap is highly correlated with CPIA gender equality and 6 other fieldsHigh correlation
unpaid domestic is highly correlated with CPIA social protection and 1 other fieldsHigh correlation
primary enrollment is highly correlated with CPIA gender equality and 4 other fieldsHigh correlation
teenage mothers is highly correlated with fertility rate and 4 other fieldsHigh correlation
married by 18 is highly correlated with fertility rate and 4 other fieldsHigh correlation
CPI human resources is highly correlated with CPIA gender equality and 1 other fieldsHigh correlation
CPIA gender equality is highly correlated with CPI human resources and 6 other fieldsHigh correlation
CPIA social protection is highly correlated with CPI human resources and 2 other fieldsHigh correlation
fertility rate is highly correlated with CPIA gender equality and 6 other fieldsHigh correlation
intentional homicides is highly correlated with teenage mothersHigh correlation
literacy rate is highly correlated with CPIA gender equality and 5 other fieldsHigh correlation
poverty gap is highly correlated with CPIA gender equality and 6 other fieldsHigh correlation
unpaid domestic is highly correlated with CPIA social protectionHigh correlation
gender parity index is highly correlated with CPIA gender equality and 5 other fieldsHigh correlation
primary enrollment is highly correlated with CPIA gender equality and 5 other fieldsHigh correlation
teenage mothers is highly correlated with fertility rate and 3 other fieldsHigh correlation
married by 18 is highly correlated with fertility rate and 5 other fieldsHigh correlation
CPI human resources is highly correlated with CPIA gender equality and 1 other fieldsHigh correlation
CPIA gender equality is highly correlated with CPI human resourcesHigh correlation
CPIA social protection is highly correlated with CPI human resourcesHigh correlation
fertility rate is highly correlated with literacy rate and 1 other fieldsHigh correlation
literacy rate is highly correlated with fertility rate and 1 other fieldsHigh correlation
poverty gap is highly correlated with fertility rate and 1 other fieldsHigh correlation
teenage mothers is highly correlated with married by 18High correlation
married by 18 is highly correlated with teenage mothersHigh correlation
df_index is highly correlated with teenage mothers and 1 other fieldsHigh correlation
CPI human resources is highly correlated with CPIA gender equality and 4 other fieldsHigh correlation
CPIA gender equality is highly correlated with CPI human resources and 6 other fieldsHigh correlation
CPIA social protection is highly correlated with CPI human resources and 3 other fieldsHigh correlation
management is highly correlated with CPIA gender equality and 5 other fieldsHigh correlation
fertility rate is highly correlated with CPIA gender equality and 7 other fieldsHigh correlation
intentional homicides is highly correlated with teenage mothersHigh correlation
labor force is highly correlated with CPIA gender equality and 8 other fieldsHigh correlation
literacy rate is highly correlated with management and 8 other fieldsHigh correlation
poverty gap is highly correlated with CPIA gender equality and 8 other fieldsHigh correlation
unpaid domestic is highly correlated with CPI human resources and 6 other fieldsHigh correlation
gender parity index is highly correlated with CPI human resources and 7 other fieldsHigh correlation
primary enrollment is highly correlated with CPI human resources and 9 other fieldsHigh correlation
teenage mothers is highly correlated with df_index and 8 other fieldsHigh correlation
married by 18 is highly correlated with df_index and 7 other fieldsHigh correlation
labor category is highly correlated with labor force and 3 other fieldsHigh correlation
CPI human resources has 5722 (82.5%) missing values Missing
CPIA gender equality has 5722 (82.5%) missing values Missing
CPIA social protection has 5730 (82.6%) missing values Missing
employers has 1516 (21.8%) missing values Missing
management has 5924 (85.4%) missing values Missing
fertility rate has 755 (10.9%) missing values Missing
intentional homicides has 4863 (70.1%) missing values Missing
labor force has 955 (13.8%) missing values Missing
literacy rate has 6091 (87.8%) missing values Missing
poverty gap has 5228 (75.3%) missing values Missing
parliment seats has 2420 (34.9%) missing values Missing
unpaid domestic has 6762 (97.4%) missing values Missing
gender parity index has 2265 (32.6%) missing values Missing
primary enrollment has 4702 (67.8%) missing values Missing
teenage mothers has 6599 (95.1%) missing values Missing
married by 18 has 6437 (92.8%) missing values Missing
labor category has 955 (13.8%) missing values Missing
df_index is uniformly distributed Uniform
Country Name is uniformly distributed Uniform
df_index has unique values Unique
intentional homicides has 121 (1.7%) zeros Zeros
parliment seats has 181 (2.6%) zeros Zeros

Reproduction

Analysis started2022-05-28 01:19:58.774671
Analysis finished2022-05-28 01:20:42.584094
Duration43.81 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct6939
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6724.60513
Minimum30
Maximum13405
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.3 KiB
2022-05-27T21:20:42.687862image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile676.9
Q13384.5
median6718
Q310072.5
95-th percentile12758.1
Maximum13405
Range13375
Interquartile range (IQR)6688

Descriptive statistics

Standard deviation3868.857506
Coefficient of variation (CV)0.5753285778
Kurtosis-1.199503177
Mean6724.60513
Median Absolute Deviation (MAD)3348
Skewness-0.002850952279
Sum46662035
Variance14968058.4
MonotonicityStrictly increasing
2022-05-27T21:20:42.841205image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
301
 
< 0.1%
89511
 
< 0.1%
89621
 
< 0.1%
89611
 
< 0.1%
89601
 
< 0.1%
89591
 
< 0.1%
89581
 
< 0.1%
89571
 
< 0.1%
89561
 
< 0.1%
89551
 
< 0.1%
Other values (6929)6929
99.9%
ValueCountFrequency (%)
301
< 0.1%
311
< 0.1%
321
< 0.1%
331
< 0.1%
341
< 0.1%
351
< 0.1%
361
< 0.1%
371
< 0.1%
381
< 0.1%
391
< 0.1%
ValueCountFrequency (%)
134051
< 0.1%
134041
< 0.1%
134031
< 0.1%
134021
< 0.1%
134011
< 0.1%
134001
< 0.1%
133991
< 0.1%
133981
< 0.1%
133971
< 0.1%
133961
< 0.1%

Country Name
Categorical

HIGH CARDINALITY
UNIFORM

Distinct217
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size54.3 KiB
Afghanistan
 
32
Myanmar
 
32
Nauru
 
32
Nepal
 
32
Netherlands
 
32
Other values (212)
6779 

Length

Max length30
Median length22
Mean length9.658884565
Min length4

Characters and Unicode

Total characters67023
Distinct characters58
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAfghanistan
2nd rowAfghanistan
3rd rowAfghanistan
4th rowAfghanistan
5th rowAfghanistan

Common Values

ValueCountFrequency (%)
Afghanistan32
 
0.5%
Myanmar32
 
0.5%
Nauru32
 
0.5%
Nepal32
 
0.5%
Netherlands32
 
0.5%
New Caledonia32
 
0.5%
New Zealand32
 
0.5%
Nicaragua32
 
0.5%
Niger32
 
0.5%
Nigeria32
 
0.5%
Other values (207)6619
95.4%

Length

2022-05-27T21:20:42.990015image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
islands286
 
2.8%
and256
 
2.5%
rep224
 
2.2%
republic192
 
1.9%
st127
 
1.3%
china96
 
1.0%
arab96
 
1.0%
the96
 
1.0%
new96
 
1.0%
guinea96
 
1.0%
Other values (255)8534
84.5%

Most occurring characters

ValueCountFrequency (%)
a9494
 
14.2%
i5182
 
7.7%
n5175
 
7.7%
e4602
 
6.9%
r3704
 
5.5%
o3325
 
5.0%
3160
 
4.7%
s2395
 
3.6%
u2368
 
3.5%
t2364
 
3.5%
Other values (48)25254
37.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter52549
78.4%
Uppercase Letter10037
 
15.0%
Space Separator3160
 
4.7%
Other Punctuation1023
 
1.5%
Open Punctuation95
 
0.1%
Close Punctuation95
 
0.1%
Dash Punctuation64
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a9494
18.1%
i5182
9.9%
n5175
9.8%
e4602
 
8.8%
r3704
 
7.0%
o3325
 
6.3%
s2395
 
4.6%
u2368
 
4.5%
t2364
 
4.5%
l2364
 
4.5%
Other values (16)11576
22.0%
Uppercase Letter
ValueCountFrequency (%)
S1119
 
11.1%
C800
 
8.0%
M797
 
7.9%
B736
 
7.3%
R704
 
7.0%
A672
 
6.7%
I637
 
6.3%
G575
 
5.7%
T512
 
5.1%
P448
 
4.5%
Other values (15)3037
30.3%
Other Punctuation
ValueCountFrequency (%)
.543
53.1%
,416
40.7%
'64
 
6.3%
Space Separator
ValueCountFrequency (%)
3160
100.0%
Open Punctuation
ValueCountFrequency (%)
(95
100.0%
Close Punctuation
ValueCountFrequency (%)
)95
100.0%
Dash Punctuation
ValueCountFrequency (%)
-64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin62586
93.4%
Common4437
 
6.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a9494
15.2%
i5182
 
8.3%
n5175
 
8.3%
e4602
 
7.4%
r3704
 
5.9%
o3325
 
5.3%
s2395
 
3.8%
u2368
 
3.8%
t2364
 
3.8%
l2364
 
3.8%
Other values (41)21613
34.5%
Common
ValueCountFrequency (%)
3160
71.2%
.543
 
12.2%
,416
 
9.4%
(95
 
2.1%
)95
 
2.1%
-64
 
1.4%
'64
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII67023
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a9494
 
14.2%
i5182
 
7.7%
n5175
 
7.7%
e4602
 
6.9%
r3704
 
5.5%
o3325
 
5.0%
3160
 
4.7%
s2395
 
3.6%
u2368
 
3.5%
t2364
 
3.5%
Other values (48)25254
37.7%

year
Real number (ℝ≥0)

Distinct32
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2005.488831
Minimum1990
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.3 KiB
2022-05-27T21:20:43.137344image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1990
5-th percentile1991
Q11997
median2005
Q32013
95-th percentile2020
Maximum2021
Range31
Interquartile range (IQR)16

Descriptive statistics

Standard deviation9.227700634
Coefficient of variation (CV)0.004601222649
Kurtosis-1.202069542
Mean2005.488831
Median Absolute Deviation (MAD)8
Skewness0.0002156480745
Sum13916087
Variance85.15045898
MonotonicityNot monotonic
2022-05-27T21:20:43.277056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
1990217
 
3.1%
1991217
 
3.1%
2020217
 
3.1%
2019217
 
3.1%
2018217
 
3.1%
2017217
 
3.1%
2016217
 
3.1%
2015217
 
3.1%
2014217
 
3.1%
2013217
 
3.1%
Other values (22)4769
68.7%
ValueCountFrequency (%)
1990217
3.1%
1991217
3.1%
1992217
3.1%
1993217
3.1%
1994217
3.1%
1995217
3.1%
1996217
3.1%
1997217
3.1%
1998217
3.1%
1999217
3.1%
ValueCountFrequency (%)
2021212
3.1%
2020217
3.1%
2019217
3.1%
2018217
3.1%
2017217
3.1%
2016217
3.1%
2015217
3.1%
2014217
3.1%
2013217
3.1%
2012217
3.1%

CPI human resources
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct8
Distinct (%)0.7%
Missing5722
Missing (%)82.5%
Infinite0
Infinite (%)0.0%
Mean3.523418242
Minimum1
Maximum4.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.3 KiB
2022-05-27T21:20:43.430450image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.5
Q13
median3.5
Q34
95-th percentile4.5
Maximum4.5
Range3.5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.6107470755
Coefficient of variation (CV)0.1733393635
Kurtosis-0.03020094607
Mean3.523418242
Median Absolute Deviation (MAD)0.5
Skewness-0.4312014692
Sum4288
Variance0.3730119902
MonotonicityNot monotonic
2022-05-27T21:20:43.532606image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3.5380
 
5.5%
4338
 
4.9%
3226
 
3.3%
4.5130
 
1.9%
2.5120
 
1.7%
219
 
0.3%
1.52
 
< 0.1%
12
 
< 0.1%
(Missing)5722
82.5%
ValueCountFrequency (%)
12
 
< 0.1%
1.52
 
< 0.1%
219
 
0.3%
2.5120
 
1.7%
3226
3.3%
3.5380
5.5%
4338
4.9%
4.5130
 
1.9%
ValueCountFrequency (%)
4.5130
 
1.9%
4338
4.9%
3.5380
5.5%
3226
3.3%
2.5120
 
1.7%
219
 
0.3%
1.52
 
< 0.1%
12
 
< 0.1%

CPIA gender equality
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct8
Distinct (%)0.7%
Missing5722
Missing (%)82.5%
Infinite0
Infinite (%)0.0%
Mean3.352095316
Minimum1.5
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.3 KiB
2022-05-27T21:20:43.636109image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile2.5
Q13
median3.5
Q34
95-th percentile4.5
Maximum5
Range3.5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.6644628055
Coefficient of variation (CV)0.198223124
Kurtosis-0.1290391282
Mean3.352095316
Median Absolute Deviation (MAD)0.5
Skewness-0.2024222719
Sum4079.5
Variance0.4415108199
MonotonicityNot monotonic
2022-05-27T21:20:43.737596image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3.5335
 
4.8%
3310
 
4.5%
4261
 
3.8%
2.5164
 
2.4%
4.583
 
1.2%
237
 
0.5%
1.517
 
0.2%
510
 
0.1%
(Missing)5722
82.5%
ValueCountFrequency (%)
1.517
 
0.2%
237
 
0.5%
2.5164
2.4%
3310
4.5%
3.5335
4.8%
4261
3.8%
4.583
 
1.2%
510
 
0.1%
ValueCountFrequency (%)
510
 
0.1%
4.583
 
1.2%
4261
3.8%
3.5335
4.8%
3310
4.5%
2.5164
2.4%
237
 
0.5%
1.517
 
0.2%

CPIA social protection
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct8
Distinct (%)0.7%
Missing5730
Missing (%)82.6%
Infinite0
Infinite (%)0.0%
Mean3.043424318
Minimum1
Maximum4.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.3 KiB
2022-05-27T21:20:43.860155image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12.5
median3
Q33.5
95-th percentile4
Maximum4.5
Range3.5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5832727823
Coefficient of variation (CV)0.1916501682
Kurtosis0.2833720957
Mean3.043424318
Median Absolute Deviation (MAD)0.5
Skewness-0.3228528381
Sum3679.5
Variance0.3402071385
MonotonicityNot monotonic
2022-05-27T21:20:43.971825image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3.5408
 
5.9%
3339
 
4.9%
2.5273
 
3.9%
287
 
1.3%
470
 
1.0%
4.518
 
0.3%
18
 
0.1%
1.56
 
0.1%
(Missing)5730
82.6%
ValueCountFrequency (%)
18
 
0.1%
1.56
 
0.1%
287
 
1.3%
2.5273
3.9%
3339
4.9%
3.5408
5.9%
470
 
1.0%
4.518
 
0.3%
ValueCountFrequency (%)
4.518
 
0.3%
470
 
1.0%
3.5408
5.9%
3339
4.9%
2.5273
3.9%
287
 
1.3%
1.56
 
0.1%
18
 
0.1%

employers
Real number (ℝ≥0)

MISSING

Distinct628
Distinct (%)11.6%
Missing1516
Missing (%)21.8%
Infinite0
Infinite (%)0.0%
Mean1.740768947
Minimum0
Maximum10.86999989
Zeros58
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size54.3 KiB
2022-05-27T21:20:44.108472image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1400000006
Q10.6000000238
median1.450000048
Q32.425000072
95-th percentile4.420000076
Maximum10.86999989
Range10.86999989
Interquartile range (IQR)1.825000048

Descriptive statistics

Standard deviation1.518537384
Coefficient of variation (CV)0.8723371285
Kurtosis5.763855435
Mean1.740768947
Median Absolute Deviation (MAD)0.8999998569
Skewness1.88282576
Sum9440.189998
Variance2.305955788
MonotonicityNot monotonic
2022-05-27T21:20:44.257474image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
058
 
0.8%
0.15000000651
 
0.7%
0.479999989344
 
0.6%
0.330000013143
 
0.6%
0.469999998840
 
0.6%
0.119999997340
 
0.6%
0.140000000639
 
0.6%
0.0500000007535
 
0.5%
0.533
 
0.5%
0.959999978532
 
0.5%
Other values (618)5008
72.2%
(Missing)1516
 
21.8%
ValueCountFrequency (%)
058
0.8%
0.0099999997765
 
0.1%
0.019999999554
 
0.1%
0.029999999336
 
0.1%
0.0399999991129
0.4%
0.0500000007535
0.5%
0.059999998669
 
0.1%
0.07000000034
 
0.1%
0.0799999982111
 
0.2%
0.090000003588
 
0.1%
ValueCountFrequency (%)
10.869999891
< 0.1%
10.819999691
< 0.1%
10.770000461
< 0.1%
10.710000041
< 0.1%
10.479999541
< 0.1%
10.470000271
< 0.1%
10.319999691
< 0.1%
10.260000231
< 0.1%
10.251
< 0.1%
10.229999541
< 0.1%

management
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct868
Distinct (%)85.5%
Missing5924
Missing (%)85.4%
Infinite0
Infinite (%)0.0%
Mean30.7026601
Minimum4.21999979
Maximum60.90999985
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.3 KiB
2022-05-27T21:20:44.396678image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum4.21999979
5-th percentile15.48800001
Q124.75
median31.45999908
Q336.625
95-th percentile43.23599968
Maximum60.90999985
Range56.69000006
Interquartile range (IQR)11.875

Descriptive statistics

Standard deviation8.761744902
Coefficient of variation (CV)0.2853741296
Kurtosis0.2124899072
Mean30.7026601
Median Absolute Deviation (MAD)5.810001373
Skewness-0.2350916434
Sum31163.2
Variance76.76817372
MonotonicityNot monotonic
2022-05-27T21:20:44.536449image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.53
 
< 0.1%
30.540000923
 
< 0.1%
34.259998323
 
< 0.1%
34.340000153
 
< 0.1%
35.580001833
 
< 0.1%
40.259998323
 
< 0.1%
33.319999693
 
< 0.1%
33.139999393
 
< 0.1%
23.110000613
 
< 0.1%
30.819999693
 
< 0.1%
Other values (858)985
 
14.2%
(Missing)5924
85.4%
ValueCountFrequency (%)
4.219999791
< 0.1%
4.2600002291
< 0.1%
4.51
< 0.1%
5.2300000191
< 0.1%
5.6999998091
< 0.1%
5.7300000191
< 0.1%
5.751
< 0.1%
6.0799999241
< 0.1%
6.4000000951
< 0.1%
7.6199998861
< 0.1%
ValueCountFrequency (%)
60.909999851
< 0.1%
59.310001371
< 0.1%
57.970001221
< 0.1%
55.830001831
< 0.1%
54.639999391
< 0.1%
54.169998171
< 0.1%
53.680000311
< 0.1%
51.830001831
< 0.1%
50.490001681
< 0.1%
50.189998631
< 0.1%

fertility rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct3241
Distinct (%)52.4%
Missing755
Missing (%)10.9%
Infinite0
Infinite (%)0.0%
Mean3.123984363
Minimum0.837
Maximum8.606
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.3 KiB
2022-05-27T21:20:44.685899image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.837
5-th percentile1.31015
Q11.796
median2.5625
Q34.321
95-th percentile6.3367
Maximum8.606
Range7.769
Interquartile range (IQR)2.525

Descriptive statistics

Standard deviation1.645491002
Coefficient of variation (CV)0.5267283094
Kurtosis-0.3178384729
Mean3.123984363
Median Absolute Deviation (MAD)0.965
Skewness0.8637256705
Sum19318.7193
Variance2.707640638
MonotonicityNot monotonic
2022-05-27T21:20:44.832352image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.4628
 
0.4%
1.627
 
0.4%
1.526
 
0.4%
1.7125
 
0.4%
1.3424
 
0.3%
1.3723
 
0.3%
1.423
 
0.3%
1.5723
 
0.3%
1.7523
 
0.3%
1.4422
 
0.3%
Other values (3231)5940
85.6%
(Missing)755
 
10.9%
ValueCountFrequency (%)
0.8371
< 0.1%
0.861
< 0.1%
0.8621
< 0.1%
0.8681
< 0.1%
0.8751
< 0.1%
0.8791
< 0.1%
0.91
< 0.1%
0.9011
< 0.1%
0.9061
< 0.1%
0.9111
< 0.1%
ValueCountFrequency (%)
8.6061
< 0.1%
8.4591
< 0.1%
8.2721
< 0.1%
8.0481
< 0.1%
7.7951
< 0.1%
7.7721
< 0.1%
7.7611
< 0.1%
7.7521
< 0.1%
7.7431
< 0.1%
7.7341
< 0.1%

intentional homicides
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct1955
Distinct (%)94.2%
Missing4863
Missing (%)70.1%
Infinite0
Infinite (%)0.0%
Mean2.34666783
Minimum0
Maximum19.17122899
Zeros121
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size54.3 KiB
2022-05-27T21:20:44.986006image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.704246345
median1.339727909
Q33.062671185
95-th percentile8.134190226
Maximum19.17122899
Range19.17122899
Interquartile range (IQR)2.35842484

Descriptive statistics

Standard deviation2.624446621
Coefficient of variation (CV)1.118371585
Kurtosis6.550194652
Mean2.34666783
Median Absolute Deviation (MAD)0.8319602577
Skewness2.318405185
Sum4871.682414
Variance6.887720065
MonotonicityNot monotonic
2022-05-27T21:20:45.136673image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0121
 
1.7%
10.52
 
< 0.1%
0.52231503951
 
< 0.1%
0.95679569121
 
< 0.1%
2.0912189721
 
< 0.1%
2.1135950011
 
< 0.1%
2.1117003841
 
< 0.1%
1.8433742081
 
< 0.1%
2.6396135611
 
< 0.1%
3.2413601161
 
< 0.1%
Other values (1945)1945
 
28.0%
(Missing)4863
70.1%
ValueCountFrequency (%)
0121
1.7%
0.064867613851
 
< 0.1%
0.084343131471
 
< 0.1%
0.1125425271
 
< 0.1%
0.12175062791
 
< 0.1%
0.12214839481
 
< 0.1%
0.12518981911
 
< 0.1%
0.13141903391
 
< 0.1%
0.14141065181
 
< 0.1%
0.15312736481
 
< 0.1%
ValueCountFrequency (%)
19.171228991
< 0.1%
18.276901851
< 0.1%
17.253034481
< 0.1%
17.158123511
< 0.1%
16.603573091
< 0.1%
15.546899271
< 0.1%
14.488466581
< 0.1%
14.343405141
< 0.1%
14.056248231
< 0.1%
13.9986141
< 0.1%

labor force
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct5655
Distinct (%)94.5%
Missing955
Missing (%)13.8%
Infinite0
Infinite (%)0.0%
Mean50.33109058
Minimum5.994999886
Maximum90.55500031
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.3 KiB
2022-05-27T21:20:45.292529image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum5.994999886
5-th percentile18.03975067
Q141.10674858
median51.06199837
Q360.75949955
95-th percentile77.92239685
Maximum90.55500031
Range84.56000042
Interquartile range (IQR)19.65275097

Descriptive statistics

Standard deviation16.48750625
Coefficient of variation (CV)0.3275809458
Kurtosis-0.1074837848
Mean50.33109058
Median Absolute Deviation (MAD)9.769001007
Skewness-0.2389285789
Sum301181.246
Variance271.8378624
MonotonicityNot monotonic
2022-05-27T21:20:45.434910image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50.167999273
 
< 0.1%
50.620998383
 
< 0.1%
60.819999693
 
< 0.1%
55.389999393
 
< 0.1%
59.721000673
 
< 0.1%
54.777999883
 
< 0.1%
51.363998413
 
< 0.1%
49.333000183
 
< 0.1%
55.291999823
 
< 0.1%
49.576000213
 
< 0.1%
Other values (5645)5954
85.8%
(Missing)955
 
13.8%
ValueCountFrequency (%)
5.9949998861
< 0.1%
6.0809998511
< 0.1%
6.0890002251
< 0.1%
6.094999792
< 0.1%
6.1149997711
< 0.1%
6.1290001871
< 0.1%
6.1380000111
< 0.1%
6.9580001831
< 0.1%
7.8819999691
< 0.1%
8.2819995881
< 0.1%
ValueCountFrequency (%)
90.555000311
< 0.1%
90.074996951
< 0.1%
89.572998051
< 0.1%
89.04900361
< 0.1%
88.50199891
< 0.1%
87.930999761
< 0.1%
87.811996461
< 0.1%
87.669998171
< 0.1%
87.527000431
< 0.1%
87.384002691
< 0.1%

literacy rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct835
Distinct (%)98.5%
Missing6091
Missing (%)87.8%
Infinite0
Infinite (%)0.0%
Mean78.50512585
Minimum4.591829777
Maximum99.99994659
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.3 KiB
2022-05-27T21:20:45.581624image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum4.591829777
5-th percentile25.74258757
Q165.78227234
median89.7141304
Q395.0799408
95-th percentile99.58349609
Maximum99.99994659
Range95.40811682
Interquartile range (IQR)29.29766846

Descriptive statistics

Standard deviation23.46639775
Coefficient of variation (CV)0.2989154848
Kurtosis0.5855508888
Mean78.50512585
Median Absolute Deviation (MAD)8.537853241
Skewness-1.286348483
Sum66572.34672
Variance550.6718235
MonotonicityNot monotonic
2022-05-27T21:20:46.393090image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99.599998476
 
0.1%
693
 
< 0.1%
98.599998473
 
< 0.1%
99.419998173
 
< 0.1%
99.738990782
 
< 0.1%
98.251983642
 
< 0.1%
81.658752441
 
< 0.1%
92.92574311
 
< 0.1%
50.917739871
 
< 0.1%
83.454872131
 
< 0.1%
Other values (825)825
 
11.9%
(Missing)6091
87.8%
ValueCountFrequency (%)
4.5918297771
< 0.1%
8.0579795841
< 0.1%
8.2254295351
< 0.1%
9.3993501661
< 0.1%
9.7430696491
< 0.1%
11.893429761
< 0.1%
12.191949841
< 0.1%
12.79642011
< 0.1%
13.933409691
< 0.1%
13.955229761
< 0.1%
ValueCountFrequency (%)
99.999946591
< 0.1%
99.9976121
< 0.1%
99.99587251
< 0.1%
99.985870361
< 0.1%
99.978408811
< 0.1%
99.97599031
< 0.1%
99.975769041
< 0.1%
99.958198551
< 0.1%
99.909477231
< 0.1%
99.907920841
< 0.1%

poverty gap
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct502
Distinct (%)29.3%
Missing5228
Missing (%)75.3%
Infinite0
Infinite (%)0.0%
Mean15.11917008
Minimum0
Maximum100
Zeros55
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size54.3 KiB
2022-05-27T21:20:46.540902image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q10.7
median6.6
Q322.6
95-th percentile59.85
Maximum100
Range100
Interquartile range (IQR)21.9

Descriptive statistics

Standard deviation19.32729747
Coefficient of variation (CV)1.278330581
Kurtosis1.366215352
Mean15.11917008
Median Absolute Deviation (MAD)6.4
Skewness1.482736149
Sum25868.9
Variance373.5444276
MonotonicityNot monotonic
2022-05-27T21:20:46.693911image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1140
 
2.0%
0.261
 
0.9%
055
 
0.8%
0.351
 
0.7%
0.741
 
0.6%
0.539
 
0.6%
0.438
 
0.5%
0.936
 
0.5%
0.626
 
0.4%
0.826
 
0.4%
Other values (492)1198
 
17.3%
(Missing)5228
75.3%
ValueCountFrequency (%)
055
 
0.8%
0.1140
2.0%
0.261
0.9%
0.351
 
0.7%
0.438
 
0.5%
0.539
 
0.6%
0.626
 
0.4%
0.741
 
0.6%
0.826
 
0.4%
0.936
 
0.5%
ValueCountFrequency (%)
1001
< 0.1%
86.61
< 0.1%
811
< 0.1%
79.71
< 0.1%
78.91
< 0.1%
78.51
< 0.1%
78.11
< 0.1%
77.81
< 0.1%
77.31
< 0.1%
76.61
< 0.1%

parliment seats
Real number (ℝ≥0)

MISSING
ZEROS

Distinct1212
Distinct (%)26.8%
Missing2420
Missing (%)34.9%
Infinite0
Infinite (%)0.0%
Mean17.45537751
Minimum0
Maximum63.75
Zeros181
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size54.3 KiB
2022-05-27T21:20:46.840565image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.204819277
Q18.849557522
median15.5
Q324.52217815
95-th percentile39.3442623
Maximum63.75
Range63.75
Interquartile range (IQR)15.67262062

Descriptive statistics

Standard deviation11.58191808
Coefficient of variation (CV)0.6635157604
Kurtosis0.184939012
Mean17.45537751
Median Absolute Deviation (MAD)7.396869245
Skewness0.736857939
Sum78880.85098
Variance134.1408265
MonotonicityNot monotonic
2022-05-27T21:20:46.983284image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0181
 
2.6%
16.6666666758
 
0.8%
11.1111111154
 
0.8%
2046
 
0.7%
1036
 
0.5%
13.3333333335
 
0.5%
12.535
 
0.5%
2535
 
0.5%
1235
 
0.5%
2234
 
0.5%
Other values (1202)3970
57.2%
(Missing)2420
34.9%
ValueCountFrequency (%)
0181
2.6%
0.332225913614
 
0.2%
0.61538461545
 
0.1%
0.66445182723
 
< 0.1%
0.66889632112
 
< 0.1%
0.91743119279
 
0.1%
1.1764705884
 
0.1%
1.190476194
 
0.1%
1.2048192776
 
0.1%
1.2345679012
 
< 0.1%
ValueCountFrequency (%)
63.754
0.1%
61.255
0.1%
56.255
0.1%
53.412969281
 
< 0.1%
53.22314053
 
< 0.1%
53.076923086
0.1%
50.549450551
 
< 0.1%
508
0.1%
49.166666671
 
< 0.1%
48.856209155
0.1%

unpaid domestic
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct147
Distinct (%)83.1%
Missing6762
Missing (%)97.4%
Infinite0
Infinite (%)0.0%
Mean17.85448271
Minimum5.02051
Maximum31.04081
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.3 KiB
2022-05-27T21:20:47.148570image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum5.02051
5-th percentile11.788292
Q115.41667
median17.29571
Q320.05833
95-th percentile25.249998
Maximum31.04081
Range26.0203
Interquartile range (IQR)4.64166

Descriptive statistics

Standard deviation4.094182139
Coefficient of variation (CV)0.2293083594
Kurtosis1.396815851
Mean17.85448271
Median Absolute Deviation (MAD)2.15682
Skewness0.3623740887
Sum3160.24344
Variance16.76232739
MonotonicityNot monotonic
2022-05-27T21:20:47.345992image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.6254
 
0.1%
20.833333
 
< 0.1%
20.555563
 
< 0.1%
14.583333
 
< 0.1%
16.253
 
< 0.1%
18.958333
 
< 0.1%
153
 
< 0.1%
15.555563
 
< 0.1%
19.791672
 
< 0.1%
17.638892
 
< 0.1%
Other values (137)148
 
2.1%
(Missing)6762
97.4%
ValueCountFrequency (%)
5.020511
< 0.1%
6.292341
< 0.1%
8.194441
< 0.1%
8.680561
< 0.1%
101
< 0.1%
10.416672
< 0.1%
10.833331
< 0.1%
11.608141
< 0.1%
11.833331
< 0.1%
12.51
< 0.1%
ValueCountFrequency (%)
31.040811
< 0.1%
29.521991
< 0.1%
29.112431
< 0.1%
28.472221
< 0.1%
27.768251
< 0.1%
27.708332
< 0.1%
26.111111
< 0.1%
25.416671
< 0.1%
25.208331
< 0.1%
24.097221
< 0.1%

gender parity index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct3923
Distinct (%)83.9%
Missing2265
Missing (%)32.6%
Infinite0
Infinite (%)0.0%
Mean0.9566850937
Minimum0
Maximum1.253069997
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size54.3 KiB
2022-05-27T21:20:47.511418image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.7476680189
Q10.9535724968
median0.9854900241
Q31.0000875
95-th percentile1.032538998
Maximum1.253069997
Range1.253069997
Interquartile range (IQR)0.04651500285

Descriptive statistics

Standard deviation0.09339480223
Coefficient of variation (CV)0.09762334842
Kurtosis12.45280335
Mean0.9566850937
Median Absolute Deviation (MAD)0.01860994101
Skewness-2.829682667
Sum4471.546128
Variance0.008722589084
MonotonicityNot monotonic
2022-05-27T21:20:47.668357image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.99711000925
 
0.1%
0.98343998195
 
0.1%
0.99836999185
 
0.1%
0.99215000875
 
0.1%
0.96894001965
 
0.1%
0.99237000944
 
0.1%
1.0007499464
 
0.1%
0.99946999554
 
0.1%
1.0015100244
 
0.1%
0.99475997694
 
0.1%
Other values (3913)4629
66.7%
(Missing)2265
32.6%
ValueCountFrequency (%)
02
< 0.1%
0.08489999921
< 0.1%
0.35306999091
< 0.1%
0.43281999231
< 0.1%
0.44242998961
< 0.1%
0.44701999431
< 0.1%
0.45388999581
< 0.1%
0.45693001151
< 0.1%
0.45978000761
< 0.1%
0.46040999891
< 0.1%
ValueCountFrequency (%)
1.2530699971
< 0.1%
1.2095600371
< 0.1%
1.2051299811
< 0.1%
1.1770600081
< 0.1%
1.1670199631
< 0.1%
1.1610399481
< 0.1%
1.1566900011
< 0.1%
1.1549899581
< 0.1%
1.1523499491
< 0.1%
1.1510100361
< 0.1%

primary enrollment
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2237
Distinct (%)100.0%
Missing4702
Missing (%)67.8%
Infinite0
Infinite (%)0.0%
Mean83.68204569
Minimum13.69764
Maximum99.98995
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.3 KiB
2022-05-27T21:20:47.818909image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum13.69764
5-th percentile45.215752
Q180.63427
median89.69032
Q394.89117
95-th percentile98.44147
Maximum99.98995
Range86.29231
Interquartile range (IQR)14.2569

Descriptive statistics

Standard deviation17.06286072
Coefficient of variation (CV)0.2039010946
Kurtosis2.603778972
Mean83.68204569
Median Absolute Deviation (MAD)5.89703
Skewness-1.759647035
Sum187196.7362
Variance291.1412159
MonotonicityNot monotonic
2022-05-27T21:20:47.955279image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99.128671
 
< 0.1%
97.773921
 
< 0.1%
97.029541
 
< 0.1%
95.612671
 
< 0.1%
95.423821
 
< 0.1%
98.786241
 
< 0.1%
99.042961
 
< 0.1%
99.400251
 
< 0.1%
97.81871
 
< 0.1%
98.210781
 
< 0.1%
Other values (2227)2227
32.1%
(Missing)4702
67.8%
ValueCountFrequency (%)
13.697641
< 0.1%
15.471241
< 0.1%
15.571761
< 0.1%
16.020551
< 0.1%
16.883371
< 0.1%
17.122351
< 0.1%
17.274081
< 0.1%
17.398231
< 0.1%
18.568851
< 0.1%
19.149151
< 0.1%
ValueCountFrequency (%)
99.989951
< 0.1%
99.964381
< 0.1%
99.960221
< 0.1%
99.93281
< 0.1%
99.894811
< 0.1%
99.891151
< 0.1%
99.88961
< 0.1%
99.83751
< 0.1%
99.80991
< 0.1%
99.804661
< 0.1%

teenage mothers
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct228
Distinct (%)67.1%
Missing6599
Missing (%)95.1%
Infinite0
Infinite (%)0.0%
Mean19.13376471
Minimum1.6
Maximum46.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.3 KiB
2022-05-27T21:20:48.096575image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.6
5-th percentile4.4
Q19.975
median18.05
Q326.45
95-th percentile37.215
Maximum46.4
Range44.8
Interquartile range (IQR)16.475

Descriptive statistics

Standard deviation10.53935851
Coefficient of variation (CV)0.5508251341
Kurtosis-0.7635241677
Mean19.13376471
Median Absolute Deviation (MAD)8.15
Skewness0.3790100614
Sum6505.48
Variance111.0780778
MonotonicityNot monotonic
2022-05-27T21:20:48.255465image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.54
 
0.1%
20.54
 
0.1%
17.64
 
0.1%
4.43
 
< 0.1%
293
 
< 0.1%
13.73
 
< 0.1%
17.53
 
< 0.1%
25.23
 
< 0.1%
13.83
 
< 0.1%
353
 
< 0.1%
Other values (218)307
 
4.4%
(Missing)6599
95.1%
ValueCountFrequency (%)
1.61
< 0.1%
2.11
< 0.1%
2.81
< 0.1%
3.41
< 0.1%
3.51
< 0.1%
3.62
< 0.1%
3.721
< 0.1%
3.81
< 0.1%
4.061
< 0.1%
4.12
< 0.1%
ValueCountFrequency (%)
46.42
< 0.1%
43.11
< 0.1%
42.91
< 0.1%
42.51
< 0.1%
41.51
< 0.1%
411
< 0.1%
40.42
< 0.1%
401
< 0.1%
39.32
< 0.1%
38.91
< 0.1%

married by 18
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct359
Distinct (%)71.5%
Missing6437
Missing (%)92.8%
Infinite0
Infinite (%)0.0%
Mean27.76379753
Minimum0
Maximum83.5
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size54.3 KiB
2022-05-27T21:20:48.405768image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.705
Q114.9
median25.5
Q338.05
95-th percentile60.585
Maximum83.5
Range83.5
Interquartile range (IQR)23.15

Descriptive statistics

Standard deviation16.93559096
Coefficient of variation (CV)0.609988275
Kurtosis-0.003174762281
Mean27.76379753
Median Absolute Deviation (MAD)11.4
Skewness0.637418689
Sum13937.42636
Variance286.8142412
MonotonicityNot monotonic
2022-05-27T21:20:48.552070image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.14
 
0.1%
224
 
0.1%
314
 
0.1%
30.54
 
0.1%
234
 
0.1%
18.53
 
< 0.1%
43.33
 
< 0.1%
35.93
 
< 0.1%
393
 
< 0.1%
18.93
 
< 0.1%
Other values (349)467
 
6.7%
(Missing)6437
92.8%
ValueCountFrequency (%)
02
< 0.1%
0.14
0.1%
0.21
 
< 0.1%
1.485925581
 
< 0.1%
1.61
 
< 0.1%
1.81
 
< 0.1%
2.21
 
< 0.1%
2.51
 
< 0.1%
3.23
< 0.1%
3.4962911
 
< 0.1%
ValueCountFrequency (%)
83.51
< 0.1%
76.61
< 0.1%
76.31
< 0.1%
74.51
< 0.1%
73.31
< 0.1%
721
< 0.1%
71.41
< 0.1%
70.61
< 0.1%
69.91
< 0.1%
68.71
< 0.1%

labor category
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)0.1%
Missing955
Missing (%)13.8%
Memory size54.3 KiB
middle labor
3807 
low labor
1427 
high labor
750 

Length

Max length12
Median length12
Mean length11.0339238
Min length9

Characters and Unicode

Total characters66027
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowlow labor
2nd rowlow labor
3rd rowlow labor
4th rowlow labor
5th rowlow labor

Common Values

ValueCountFrequency (%)
middle labor3807
54.9%
low labor1427
 
20.6%
high labor750
 
10.8%
(Missing)955
 
13.8%

Length

2022-05-27T21:20:48.687154image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-27T21:20:48.814577image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
labor5984
50.0%
middle3807
31.8%
low1427
 
11.9%
high750
 
6.3%

Most occurring characters

ValueCountFrequency (%)
l11218
17.0%
d7614
11.5%
o7411
11.2%
5984
9.1%
a5984
9.1%
b5984
9.1%
r5984
9.1%
i4557
6.9%
m3807
 
5.8%
e3807
 
5.8%
Other values (3)3677
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter60043
90.9%
Space Separator5984
 
9.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l11218
18.7%
d7614
12.7%
o7411
12.3%
a5984
10.0%
b5984
10.0%
r5984
10.0%
i4557
7.6%
m3807
 
6.3%
e3807
 
6.3%
h1500
 
2.5%
Other values (2)2177
 
3.6%
Space Separator
ValueCountFrequency (%)
5984
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin60043
90.9%
Common5984
 
9.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
l11218
18.7%
d7614
12.7%
o7411
12.3%
a5984
10.0%
b5984
10.0%
r5984
10.0%
i4557
7.6%
m3807
 
6.3%
e3807
 
6.3%
h1500
 
2.5%
Other values (2)2177
 
3.6%
Common
ValueCountFrequency (%)
5984
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII66027
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l11218
17.0%
d7614
11.5%
o7411
11.2%
5984
9.1%
a5984
9.1%
b5984
9.1%
r5984
9.1%
i4557
6.9%
m3807
 
5.8%
e3807
 
5.8%
Other values (3)3677
 
5.6%

Interactions

2022-05-27T21:20:39.321529image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:02.116602image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:04.362891image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:07.114152image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:09.255375image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:11.404145image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:13.621218image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:15.708199image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:18.133337image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:20.310446image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:22.456960image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:24.579317image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:26.600996image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:28.637147image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:31.282451image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:33.192927image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:35.354807image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:37.423584image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:39.435052image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:02.253325image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:04.492776image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:07.240935image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:09.379741image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:11.529497image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:13.754589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:15.831018image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:18.260084image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:20.439625image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:22.571251image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:24.703690image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:26.718224image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:28.759399image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:31.389284image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:33.318250image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:35.475079image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:37.534986image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:39.545334image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:02.376453image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:05.114719image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:07.369734image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:09.504536image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:11.658596image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:13.875314image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:15.957652image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:18.383613image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:20.559993image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:22.684425image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:24.824973image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:26.835677image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:28.870333image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:31.504618image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:33.439377image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:35.594305image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:37.645680image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:39.665400image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:02.504676image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:05.257752image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:07.500582image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:09.631594image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:11.795782image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:14.000199image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:16.057112image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:18.513222image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:20.678082image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:22.811768image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:24.926224image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:26.943007image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:28.994706image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:31.621825image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:33.565154image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:35.714805image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-05-27T21:20:39.786037image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-05-27T21:20:07.633408image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:09.761526image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:11.929937image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:14.122907image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:16.561058image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:18.641366image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:20.793965image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:22.940254image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:25.030052image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:27.055127image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:29.120322image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:31.731525image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:33.703800image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:35.840594image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:37.854257image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:39.907334image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:02.764884image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:05.524053image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:07.764046image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:09.890826image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:12.063362image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:14.247333image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-05-27T21:20:20.901585image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-05-27T21:20:25.139745image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:27.169649image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:29.245245image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:31.849802image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:33.844514image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:35.963994image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:37.959466image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:40.013862image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:02.880456image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:05.638496image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:07.886747image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:10.012294image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:12.187434image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:14.359110image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:16.793877image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:18.889888image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:21.023247image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:23.180055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:25.258957image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:27.281682image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:29.359359image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:31.950060image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:33.959739image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:36.074842image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:38.062175image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:40.113039image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:03.002362image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-27T21:20:05.765465image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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Correlations

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Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-27T21:20:49.171068image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-27T21:20:49.401423image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-27T21:20:50.209354image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-27T21:20:41.424099image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-27T21:20:41.785313image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-05-27T21:20:42.100916image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-05-27T21:20:42.445936image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Indicator Namedf_indexCountry NameyearCPI human resourcesCPIA gender equalityCPIA social protectionemployersmanagementfertility rateintentional homicideslabor forceliteracy ratepoverty gapparliment seatsunpaid domesticgender parity indexprimary enrollmentteenage mothersmarried by 18labor category
030Afghanistan1990.0NaNNaNNaNNaNNaN7.466NaN15.180NaNNaNNaNNaN0.54850NaNNaNNaNlow labor
131Afghanistan1991.0NaNNaNNaN0.05NaN7.479NaN15.214NaNNaNNaNNaN0.54788NaNNaNNaNlow labor
232Afghanistan1992.0NaNNaNNaN0.05NaN7.502NaN15.223NaNNaNNaNNaNNaNNaNNaNNaNlow labor
333Afghanistan1993.0NaNNaNNaN0.05NaN7.535NaN15.197NaNNaNNaNNaN0.3530713.69764NaNNaNlow labor
434Afghanistan1994.0NaNNaNNaN0.05NaN7.572NaN15.178NaNNaNNaNNaN0.50580NaNNaNNaNlow labor
535Afghanistan1995.0NaNNaNNaN0.05NaN7.606NaN15.221NaNNaNNaNNaN0.51505NaNNaNNaNlow labor
636Afghanistan1996.0NaNNaNNaN0.05NaN7.629NaN15.078NaNNaNNaNNaNNaNNaNNaNNaNlow labor
737Afghanistan1997.0NaNNaNNaN0.05NaN7.632NaN14.954NaNNaNNaNNaNNaNNaNNaNNaNlow labor
838Afghanistan1998.0NaNNaNNaN0.05NaN7.610NaN14.873NaNNaNNaNNaNNaNNaNNaNNaNlow labor
939Afghanistan1999.0NaNNaNNaN0.04NaN7.561NaN14.827NaNNaNNaNNaN0.08490NaNNaNNaNlow labor

Last rows

Indicator Namedf_indexCountry NameyearCPI human resourcesCPIA gender equalityCPIA social protectionemployersmanagementfertility rateintentional homicideslabor forceliteracy ratepoverty gapparliment seatsunpaid domesticgender parity indexprimary enrollmentteenage mothersmarried by 18labor category
692913396Zimbabwe2012.02.03.02.00.32NaN4.058NaN78.475998NaNNaN14.953271NaN0.98006NaNNaNNaNhigh labor
693013397Zimbabwe2013.02.53.02.00.32NaN4.030NaN79.411003NaNNaN31.481481NaN0.97872NaNNaNNaNhigh labor
693113398Zimbabwe2014.03.54.02.50.30NaN3.974NaN80.31400388.283829NaN31.481481NaNNaNNaNNaN33.500000high labor
693213399Zimbabwe2015.03.54.02.50.31NaN3.896NaN80.299004NaNNaN31.481481NaN0.97974NaN21.632.400000high labor
693313400Zimbabwe2016.03.54.02.50.31NaN3.804NaN80.279999NaNNaN31.481481NaN0.98341NaNNaNNaNhigh labor
693413401Zimbabwe2017.04.04.02.50.29NaN3.707NaN80.285004NaN45.232.575758NaN0.99222NaNNaNNaNhigh labor
693513402Zimbabwe2018.04.04.03.00.28NaN3.615NaN80.308998NaNNaN31.481481NaN0.99664NaNNaNNaNhigh labor
693613403Zimbabwe2019.04.04.03.00.2528.073.531NaN80.338997NaN48.431.851852NaN0.99917NaNNaN33.658057high labor
693713404Zimbabwe2020.04.04.03.0NaNNaN3.460NaN78.980003NaNNaN31.851852NaN1.00511NaNNaNNaNhigh labor
693813405Zimbabwe2021.0NaNNaNNaNNaNNaNNaNNaN79.307999NaNNaN31.851852NaNNaNNaNNaNNaNhigh labor